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Proceedings Paper

Pose invariant face recognition for video surveillance system using kernel principle component analysis
Author(s): Sepehr Damavandinejadmonfared; Waled Hussein Al-Arashi; Shahrel Azmin Suandi
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Paper Abstract

Kernel Entropy Component Analysis (KECA) is a newer method than Kernel Principle Component Analysis (KPCA) for data transformation and dimensionality reduction in case of face recognition. Although in almost all previous researches using KECA are shown to be more superior and more appropriate method compared to KPCA, here in this paper the significance of Kernel PCA in handling face pose in surveillance images is compared to KECA. Comparative analysis is made to signify the importance of Kernel Principle Component Analysis in terms of pose invariant face recognition in surveillance.

Paper Details

Date Published: 8 June 2012
PDF: 5 pages
Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 833439 (8 June 2012); doi: 10.1117/12.956494
Show Author Affiliations
Sepehr Damavandinejadmonfared, Univ. Sains Malaysia (Malaysia)
Waled Hussein Al-Arashi, Univ. Sains Malaysia (Malaysia)
Shahrel Azmin Suandi, Univ. Sains Malaysia (Malaysia)

Published in SPIE Proceedings Vol. 8334:
Fourth International Conference on Digital Image Processing (ICDIP 2012)
Mohamed Othman; Sukumar Senthilkumar; Xie Yi, Editor(s)

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